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We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

Python3入门人工智能 掌握机器学习+深度学习，完整版11章附源码

CIFAR-10 是一个用于识别普适物体的小型数据集。一共包含10 个类别的RGB 彩色图片，每个图片的尺寸为32 × 32 ，每个类别有6000个图像，数据集中一共有50000 张训练图片和10000 张测试图片。

kaggle机器学习、深度学习竞赛，包含多模态、目标检测、视频监测和分类、图像分割、图像分类、图像检索、NLP 共21个方案

该数据集包含了4317张图片，包含雏菊、蒲公英、玫瑰、向日葵、郁金香五种花卉，可用于卷积神经网络图像分类的学习，我已将数据集拆分为训练集和测试集两部分，有需要可自行下载

说明：本教程将阐述无监督特征学习和深入学习的主要观点。通过学习，你也将实现多个功能 学习/深度学习算法，能看到它们为你工作，并学习如何应用/适应这些想法到新问题上。

吴恩达Coursera深度学习课程 deeplearning.ai (2-1) 深度学习实践--作业（可执行源码）

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